For many learning problems one may not have access to fine grained label information; eg,
an image can be labeled as husky, dog, or even animal depending on the expertise of the …

Generative adversarial networks (GANs) are a widely used framework for learning
generative models. Wasserstein GANs (WGANs), one of the most successful variants of …

Motivated by the recent empirical successes of deep generative models, we study the
computational complexity of the following unsupervised learning problem. For an unknown …

Incorporating a so-called “momentum” dynamic in gradient descent methods is widely used
in neural net training as it has been broadly observed that, at least empirically, it often leads …

A conditional generative model is a method for sampling from a conditional distribution $ p
(y\mid x) $. For example, one may want to sample an image of a cat given the label``cat''. A …

We consider the problem of learning high dimensional polynomial transformations of
Gaussians. Given samples of the form f (x), where x∼ N (0, I r) is hidden and f: ℝ r→ ℝ d is a …

We analyze the learning problem of one-hidden-layer nonoverlapping convolutional neural
networks with the rectified linear unit (ReLU) activation function from the perspective of …

Mixtures of high dimensional Gaussian distributions have been studied extensively in
statistics and learning theory. While the total variation distance appears naturally in the …

We provide a convergence analysis of gradient descent for the problem of agnostically
learning a single ReLU function under Gaussian distributions. Unlike prior work that studies …

The modeling of probability distributions, specifically generative modeling and density
estimation, has become an immensely popular subject in recent years by virtue of its …